Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System

Abstract

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

“…The assimilation framework provides the environment for ensemble simulations and different ensemble‐based Kalman filters. The assimilation methodology used here is the local error‐subspace transform Kalman filter (LESTKF; Nerger et al, ), which was, for example, applied by Chen et al () for assimilating sea ice concentration and thickness. Compared to the classical EnKF (Evensen, ), the LESTKF is computationally more efficient because it directly accounts for the fact that the degrees of freedom for the assimilation are given by the ensemble size.…”

Global Assimilation of Ocean‐Color Data of Phytoplankton Functional Types: Impact of Different Data Sets

“…For this, the Parallel Data Assimilation Framework (Nerger & Hiller, 2013; http:// pdaf.awi.de) was coupled online to MITgcm-REcoM2. The same data assimilation methodology was used in Chen et al (2017), Mu et al (2018), Pradhan et al (2019), and Goodliff et al (2019). This filter is computationally more efficient and has lower sampling error than the EnKF (Evensen, 1994).…”

Towards reliable Arctic sea ice prediction using multivariate data assimilation

“…They found that the assimilation of the ice thickness has an obvious impact on the simulated sea ice fields, leading to significant reduction in the bias of the modeled ice thickness for several months. Collow et al [110] and Chen et al [111], respectively, have assimilated sea ice thickness from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, an Arctic sea ice reanalysis [112]) and the combined CryoSat2 and SMOS retrievals, in the NCEP Climate Forecast System. The results showed that the experiment assimilating ice thickness results in a significant reduction of systematic bias in the forecasted ice thickness relative to the experiment assimilating ice concentration only or without sea ice assimilation.…”